Multi-scale feature learning on pixels and super-pixels for seminal vesicles MRI segmentation

Qinquan Gao, Akshay Asthana, Tong Tong, Daniel Rueckert, Philip Eddie Edwards

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

We propose a learning-based approach to segment the seminal vesicles (SV) via random forest classifiers. The proposed discriminative approach relies on the decision forest using high-dimensional multi-scale context-aware spatial, textual and descriptor-based features at both pixel and super-pixel level. After affine transformation to a template space, the relevant high-dimensional multi-scale features are extracted and random forest classifiers are learned based on the masked region of the seminal vesicles from the most similar atlases. Using these classifiers, an intermediate probabilistic segmentation is obtained for the test images. Then, a graph-cut based refinement is applied to this intermediate probabilistic representation of each voxel to get the final segmentation. We apply this approach to segment the seminal vesicles from 30 MRI T2 training images of the prostate, which presents a particularly challenging segmentation task. The results show that the multi-scale approach and the augmentation of the pixel based features with the super-pixel based features enhances the discriminative power of the learnt classifier which leads to a better quality segmentation in some very difficult cases. The results are compared to the radiologist labeled ground truth using leave-one-out cross-validation. Overall, the Dice metric of 0:7249 and Hausdorff surface distance of 7:0803 mm are achieved for this difficult task.

Original languageEnglish
Title of host publicationMedical Imaging 2014
Subtitle of host publicationImage Processing
PublisherSPIE
ISBN (Print)9780819498274
DOIs
StatePublished - 2014
Externally publishedYes
EventMedical Imaging 2014: Image Processing - San Diego, CA, United States
Duration: 16 Feb 201418 Feb 2014

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9034
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2014: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA
Period16/02/1418/02/14

Keywords

  • Graph Cut
  • Multi-scale feature
  • Random Forest Classifier
  • Superpixel

Fingerprint

Dive into the research topics of 'Multi-scale feature learning on pixels and super-pixels for seminal vesicles MRI segmentation'. Together they form a unique fingerprint.

Cite this